# Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra

arXiv: https://arxiv.org/abs/2201.02696 · HTML: https://mlsanity.com/paper/2201.02696/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [Analytical Modelling of Exoplanet Transit Specroscopy with Dimensional Analysis and Symbolic Regression](https://mlsanity.com/paper/2112.11600/) (2021)
- [An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval](https://mlsanity.com/paper/1905.10659/) (2019)
- [Peeking inside the Black Box: Interpreting Deep Learning Models for Exoplanet Atmospheric Retrievals](https://mlsanity.com/paper/2011.11284/) (2020)
- [Incorporating Physical Knowledge into Machine Learning for Planetary Space Physics](https://mlsanity.com/paper/2006.01927/) (2020)

## Built on by (most cited first, 3 of 3)

- [Reproducing Bayesian Posterior Distributions for Exoplanet Atmospheric Parameter Retrievals with a Machine Learning Surrogate Model](https://mlsanity.com/paper/2310.10521/) (2023)
- [A review of unsupervised learning in astronomy](https://mlsanity.com/paper/2406.17316/) (2024)
- [PGNAA Spectral Classification of Metal with Density Estimations](https://mlsanity.com/paper/2208.13836/) (2022)

## Abstract (arXiv)

Transit spectroscopy is a powerful tool to decode the chemical composition of the atmospheres of extrasolar planets. In this paper we focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We demonstrate methods for i) cleaning and validating the data, ii) initial exploratory data analysis based on summary statistics (estimates of location and variability), iii) exploring and quantifying the existing correlations in the data, iv) pre-processing and linearly transforming the data to its principal components, v) dimensionality reduction and manifold learning, vi) clustering and anomaly detection, vii) visualization and interpretation of the data. To illustrate the proposed unsupervised methodology, we use a well-known public benchmark data set of synthetic transit spectra. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We explore a number of different techniques for such dimensionality reduction and identify several suitable options in terms of summary statistics, principal components, etc. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes of the underlying atmospheres. We demonstrate that those branches can be successfully recovered with a K-means clustering algorithm in fully unsupervised fashion. We advocate for a three-dimensional representation of the spectroscopic data in terms of the first three principal components, in order to reveal the existing structure in the data and quickly characterize the chemical class of a planet.